活跃配电网络中的实时中断管理使用了对图表的强化学习.
Roshni Anna Jacob1, Steve Paul2, Souma Chowdhury3,4
1Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, 75080, USA.
Nature communications
|June 4, 2024
概括
本研究介绍了用于智能电网中断管理的图形强化学习模型,提高了弹性. 该模型优化了电力恢复,大大减少了中断期间的能量损失.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 智能电网需要智能控制,以快速应对故障.
- 传统的停电管理对于智能电网来说是缓慢且计算效率低下的.
- 配电网络的弹性对于尽量减少中断至关重要.
研究的目的:
- 开发一个图形增强学习模型,以加强智能电网中断管理.
- 提高发电网对停电的抵御能力.
- 解决智能电网环境中传统决策模型的局限性.
主要方法:
- 开发了一个图形强化学习模型,将中断管理视为图形学习问题.
- 使用基于囊的图形神经网络来学习恢复电源的最佳控制策略.
- 该模型明确考虑了网络拓和状态变量之间的相互依赖.
主要成果:
- 该模型在经过修改的IEEE测试网络 (13,34,123bus) 上实现了近乎最佳的实时性能.
- 显著提高了弹性,能源损耗减少了607.45千瓦 (13公交车) 和596.52千瓦 (34公交车).
- 该模型显示在各种中断场景中具有普遍性.
结论:
- 拟议的图形增强学习模型通过智能中断管理有效提高智能电网的弹性.
- 这种方法为电力恢复提供了计算效率高和快速起作用的解决方案.
- 这种方法为管理发电网的复杂相互依赖提供了一个强大的框架.
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